How do you use TfidfVectorizer in Scikit-learn to transform text data for a classification task

Question

With the help of proper code can you tell me How do you use TfidfVectorizer in Scikit-learn to transform text data for a classification task?

Answer 1

You can transform text data for classification using TfidfVectorizer in Scikit-learn to convert text into numerical features based on term frequency-inverse document frequency.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• TfidfVectorizer() converts text data into a numerical matrix of TF-IDF features.
• fit_transform(texts) learns the vocabulary and transforms the text data into a feature matrix.
• Logistic regression is used to classify the transformed text data.
• accuracy_score() evaluates model performance on test data.

Hence, TfidfVectorizer effectively transforms text into meaningful numerical features, enabling traditional machine learning models to perform classification tasks on text data. Let me know if you’d like any changes or explanations!